arXiv subjects

Didier Benoit

Publications and source records attributed to Didier Benoit.

4 recordsLinked to original sources

arXiv2026

A Fast and Generic Energy-Shifting Transformer for Hybrid Monte Carlo Radiotherapy Calculation

We introduce a novel learning framework for accelerated Monte Carlo (MC) dose calculation termed Energy-Shifting. This approach leverages deep learning to synthesize highly complex polyenergetic dose distributions directly from simple monoenergetic inputs under identical beam configurations. Unlike conventional denoising techniques, which rely on noisy low-count dose maps that compromise beam profile integrity, our method achieves superior cross-domain generalization on unseen datasets by integrating high-fidelity anatomical textures and source-specific beam similarity into the model's input space. Furthermore, we propose a novel 3D architecture termed TransUNetSE3D, featuring Transformer blocks for global context and Residual Squeeze-and-Excitation (SE) modules for adaptive channel-wise feature recalibration. Hierarchical representations of these blocks are fused into the network's latent space alongside the primary dose-map parameters, allowing physics-aware reconstruction. This hybrid design outperforms existing UNet and Transformer-based benchmarks in both spatial precision and structural preservation, while maintaining the execution speed necessary for real-time use. Our proposed pipeline achieves a Gamma Passing Rate exceeding 98% (3%/3mm) compared to the MC reference, evaluated within the framework of a treatment planning system (TPS) using 6MV TrueBeam Lineac Accelerator (LINAC) for prostate radiotherapy. These results offer a robust solution for fast volumetric dosimetry in adaptive radiotherapy.

physics.med-ph↗

arXiv2025

Machine Learning-Based Modeling of the Anode Heel Effect in X-ray Beam Monte Carlo Simulations

To develop a machine learning-based framework for accurately modeling the anode heel effect in Monte Carlo simulations of X-ray imaging systems, enabling realistic beam intensity profiles with minimal experimental calibration. Multiple regression models were trained to predict spatial intensity variations along the anode-cathode axis using experimentally acquired weights derived from beam measurements across different tube potentials. These weights captured the asymmetry introduced by the anode heel effect. A systematic fine-tuning protocol was established to minimize the number of required measurements while preserving model accuracy. The models were implemented in the OpenGATE 10 and GGEMS Monte Carlo toolkits to evaluate their integration feasibility and predictive performance. Among the tested models, gradient boosting regression (GBR) delivered the highest accuracy, with prediction errors remaining below 5% across all energy levels. The optimized fine-tuning strategy required only six detector positions per energy level, reducing measurement effort by 65%. The maximum error introduced through this fine-tuning process remained below 2%. Dose actor comparisons within Monte Carlo simulations demonstrated that the GBR-based model closely replicated clinical beam profiles and significantly outperformed conventional symmetric beam models. This study presents a robust and generalizable method for incorporating the anode heel effect into Monte Carlo simulations using machine learning. By enabling accurate, energy-dependent beam modeling with limited calibration data, the approach enhances simulation realism for applications in clinical dosimetry, image quality assessment, and radiation protection.

physics.med-ph↗

arXiv2024

Deep Learning-Based Beamlet Model for Generic X-Ray Beam Dose Calculation

Modeling the absorbed dose during X-ray imaging is essential for optimizing radiation exposure. Monte Carlo simulations (MCS) are the gold standard for precise 3D dose estimation but require significant computation time. Deep learning offers faster dose prediction but often lacks generality, as models are typically trained for specific anatomical sites and beam geometries. The aim in this work was proposing a generic deep-learning approach for dose calculation that can be used for multiple X-ray imaging systems. This article proposes a versatile approach combining beamlet decomposition with deep learning, where the X-ray beam is broken down into beamlets. By using a sampling approach, various beam shapes can be generated, reducing learning complexity. The model learns the dose response of a beamlet for different energies and patient properties, making it adaptable to new system geometries without altering the learning model. In this work, we propose combining two U-Net networks (1D+3D) trained on different body parts to predict the dose of a beamlet regardless of its orientation and energy. Results have shown that the deep learning-based dose engine achieved a relative dose error of approximately 1.2+/-3.87% compared to the reference dose. For a more realistic simulation in cone-beam CT, dose results exhibited a relative error within the beam of 5% compared to a full MCS. The convergence of the proposed method was faster compared to MCS, with a speedup of 130 times for equivalent dose results. The versatility of the proposed solution allows for the simulation of multiple X-ray systems without the need to retrain the deep learning model with new beam specificities. The same trained model is capable of calculating the 3D dose within the patient for helical CT, cone-beam CT, fan-beam CT, or any collimated beam shape.

physics.med-ph↗

arXiv2020

DUG-RECON: A Framework for Direct Image Reconstruction using Convolutional Generative Networks

This paper explores convolutional generative networks as an alternative to iterative reconstruction algorithms in medical image reconstruction. The task of medical image reconstruction involves mapping of projection main data collected from the detector to the image domain. This mapping is done typically through iterative reconstruction algorithms which are time consuming and computationally expensive. Trained deep learning networks provide faster outputs as proven in various tasks across computer vision. In this work we propose a direct reconstruction framework exclusively with deep learning architectures. The proposed framework consists of three segments, namely denoising, reconstruction and super resolution. The denoising and the super resolution segments act as processing steps. The reconstruction segment consists of a novel double U-Net generator (DUG) which learns the sinogram-to-image transformation. This entire network was trained on positron emission tomography (PET) and computed tomography (CT) images. The reconstruction framework approximates two-dimensional (2-D) mapping from projection domain to image domain. The architecture proposed in this proof-of-concept work is a novel approach to direct image reconstruction; further improvement is required to implement it in a clinical setting.

physics.med-ph↗